package net.bwie.realtime.jtp.dws.log.job;

import com.alibaba.fastjson.JSON;
import net.bwie.realtime.jtp.dws.log.bean.PageViewBean;
import net.bwie.realtime.jtp.dws.log.function.PageViewBeanMapFunction;
import net.bwie.realtime.jtp.dws.log.function.PageViewReportReduceFunction;
import net.bwie.realtime.jtp.dws.log.function.PageViewReportwindowFunction;
import net.bwie.realtime.jtp.dws.log.function.PageViewWindowFunction;
import net.bwie.realtime.jtp.utils.DorisUtil;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.api.windowing.windows.Window;

import java.time.Duration;

public class JtpTrafficPageViewMinuteWindowDwsJob {
    public static void main(String[] args) throws Exception {
        // 1.执行环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.enableCheckpointing(3000L);

        // 2.数据源-source
        DataStream<String> pageStream= KafkaUtil.consumerKafka(env, "dwd-traffic-page-log");
//        pageStream.print("page");


        // 3.数据转换-transformation
        DataStream<String> resultStream = handle(pageStream);
//        resultStream.print("result");

        // 4.数据输出-sink
        DorisUtil.saveToDoris(
                resultStream,"jtp_realtime_report","dws_traffic_page_view_window_report"
        );


        // 5.触发执行-execute
        env.execute("JtpTrafficPageViewMinuteWindowDwsJob");

    }

    private static DataStream<String> handle(DataStream<String> stream) {
        // s1-按照mid设备id分组，用于计算UV，使用状态State记录今日是否第一次访问
        KeyedStream<String, String> midStream = stream.keyBy(
                json -> JSON.parseObject(json).getJSONObject("common").getString("mid")
        );
        // s2-将流中每条日志数据封装实体类Bean对象
        DataStream<PageViewBean> beanStream = midStream.map(new PageViewBeanMapFunction());
        // s3-事件时间字段和水位线
        DataStream<PageViewBean> timeStream = beanStream.assignTimestampsAndWatermarks(
                WatermarkStrategy
                .<PageViewBean>forBoundedOutOfOrderness(Duration.ofSeconds(0))
                .withTimestampAssigner(
                        new SerializableTimestampAssigner<PageViewBean>() {
                            @Override
                            public long extractTimestamp(PageViewBean element, long l) {
                                return element.getTs();
                            }
                        }
                )
        );
        // s4-分组keyBy: ar地区、ba品牌、ch渠道、is_new新老访客
        KeyedStream<PageViewBean, String> keyedStream = timeStream.keyBy(
                bean -> bean.getBrand() + "," + bean.getChannel() + "," + bean.getProvince() + "," + bean.getIsNew()
        );
        // s5-开窗: 滚动窗口：滚动窗口大小为1分钟
        WindowedStream<PageViewBean, String, TimeWindow> windowStream = keyedStream.window(
                TumblingEventTimeWindows.of(Time.minutes(1))
        );
        // s6-聚合: 对窗口中数据计算
//        DataStream<String> reportStream = windowStream.apply(new PageViewWindowFunction());

        // s7-窗口数据聚合计算
        SingleOutputStreamOperator<String> reportStream = windowStream.reduce(
                //增量计算
                new PageViewReportReduceFunction(),
                //窗口计算：添加窗口信息
                new PageViewReportwindowFunction()
        );

        return reportStream;
    }
}
